{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(45)\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Cost_start_Bus</th>\n",
       "      <th>Days_reg_bus</th>\n",
       "      <th>Bus_tax_rate</th>\n",
       "      <th>Hours_do_tax</th>\n",
       "      <th>Ease_Bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2016</td>\n",
       "      <td>37622.207460</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>561.778746</td>\n",
       "      <td>19.9</td>\n",
       "      <td>3</td>\n",
       "      <td>48.3</td>\n",
       "      <td>275.0</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AG</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2016</td>\n",
       "      <td>14353.378810</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9</td>\n",
       "      <td>41.9</td>\n",
       "      <td>207.0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>4146.896250</td>\n",
       "      <td>10.1</td>\n",
       "      <td>5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>261.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AM</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>2016</td>\n",
       "      <td>3606.152057</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3</td>\n",
       "      <td>18.5</td>\n",
       "      <td>313.0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code               country  year  GDP_per_capita  Cost_start_Bus  \\\n",
       "0   AE  United Arab Emirates  2016    37622.207460            13.0   \n",
       "1   AF           Afghanistan  2016      561.778746            19.9   \n",
       "2   AG   Antigua and Barbuda  2016    14353.378810             9.4   \n",
       "3   AL               Albania  2016     4146.896250            10.1   \n",
       "4   AM               Armenia  2016     3606.152057             0.9   \n",
       "\n",
       "   Days_reg_bus  Bus_tax_rate  Hours_do_tax  Ease_Bus  \n",
       "0             4          15.9          12.0        26  \n",
       "1             3          48.3         275.0       183  \n",
       "2             9          41.9         207.0       113  \n",
       "3             5          36.5         261.0        58  \n",
       "4             3          18.5         313.0        38  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator = pd.read_csv('indicator1.csv')\n",
    "indicator.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the code column and year column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Cost_start_Bus</th>\n",
       "      <th>Days_reg_bus</th>\n",
       "      <th>Bus_tax_rate</th>\n",
       "      <th>Hours_do_tax</th>\n",
       "      <th>Ease_Bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>37622.207460</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>561.778746</td>\n",
       "      <td>19.9</td>\n",
       "      <td>3</td>\n",
       "      <td>48.3</td>\n",
       "      <td>275.0</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>14353.378810</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9</td>\n",
       "      <td>41.9</td>\n",
       "      <td>207.0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>4146.896250</td>\n",
       "      <td>10.1</td>\n",
       "      <td>5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>261.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>3606.152057</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3</td>\n",
       "      <td>18.5</td>\n",
       "      <td>313.0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                country  GDP_per_capita  Cost_start_Bus  Days_reg_bus  \\\n",
       "0  United Arab Emirates    37622.207460            13.0             4   \n",
       "1           Afghanistan      561.778746            19.9             3   \n",
       "2   Antigua and Barbuda    14353.378810             9.4             9   \n",
       "3               Albania     4146.896250            10.1             5   \n",
       "4               Armenia     3606.152057             0.9             3   \n",
       "\n",
       "   Bus_tax_rate  Hours_do_tax  Ease_Bus  \n",
       "0          15.9          12.0        26  \n",
       "1          48.3         275.0       183  \n",
       "2          41.9         207.0       113  \n",
       "3          36.5         261.0        58  \n",
       "4          18.5         313.0        38  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator.drop(indicator.columns[[0, 2]], axis=1, inplace=True)\n",
    "indicator.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 186 entries, 0 to 185\n",
      "Data columns (total 7 columns):\n",
      "country           186 non-null object\n",
      "GDP_per_capita    186 non-null float64\n",
      "Cost_start_Bus    186 non-null float64\n",
      "Days_reg_bus      186 non-null int64\n",
      "Bus_tax_rate      186 non-null float64\n",
      "Hours_do_tax      186 non-null float64\n",
      "Ease_Bus          186 non-null int64\n",
      "dtypes: float64(4), int64(2), object(1)\n",
      "memory usage: 10.2+ KB\n"
     ]
    }
   ],
   "source": [
    "indicator.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use describe() method to show the summary statistics of numeric attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Cost_start_Bus</th>\n",
       "      <th>Days_reg_bus</th>\n",
       "      <th>Bus_tax_rate</th>\n",
       "      <th>Hours_do_tax</th>\n",
       "      <th>Ease_Bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12716.920054</td>\n",
       "      <td>25.294086</td>\n",
       "      <td>6.951613</td>\n",
       "      <td>40.782796</td>\n",
       "      <td>251.763441</td>\n",
       "      <td>95.306452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17685.942168</td>\n",
       "      <td>46.486582</td>\n",
       "      <td>3.167883</td>\n",
       "      <td>20.429026</td>\n",
       "      <td>206.249372</td>\n",
       "      <td>54.680474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>285.727442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1721.056308</td>\n",
       "      <td>2.225000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.350000</td>\n",
       "      <td>147.250000</td>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4839.407082</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>38.100000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>95.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15052.379060</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>48.650000</td>\n",
       "      <td>275.750000</td>\n",
       "      <td>142.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>102831.321500</td>\n",
       "      <td>422.400000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>216.500000</td>\n",
       "      <td>2038.000000</td>\n",
       "      <td>189.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GDP_per_capita  Cost_start_Bus  Days_reg_bus  Bus_tax_rate  \\\n",
       "count      186.000000      186.000000    186.000000    186.000000   \n",
       "mean     12716.920054       25.294086      6.951613     40.782796   \n",
       "std      17685.942168       46.486582      3.167883     20.429026   \n",
       "min        285.727442        0.000000      1.000000      8.500000   \n",
       "25%       1721.056308        2.225000      5.000000     30.350000   \n",
       "50%       4839.407082       11.300000      7.000000     38.100000   \n",
       "75%      15052.379060       27.150000      9.000000     48.650000   \n",
       "max     102831.321500      422.400000     20.000000    216.500000   \n",
       "\n",
       "       Hours_do_tax    Ease_Bus  \n",
       "count    186.000000  186.000000  \n",
       "mean     251.763441   95.306452  \n",
       "std      206.249372   54.680474  \n",
       "min       12.000000    1.000000  \n",
       "25%      147.250000   47.500000  \n",
       "50%      209.000000   95.500000  \n",
       "75%      275.750000  142.750000  \n",
       "max     2038.000000  189.000000  "
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count, mean, min and max rows are self-explanatory. The std shows standard deviation. The 25%, 50% and 75% rows show the corresponding percentiles.\n",
    "\n",
    "To get a feel of what type of the data we are dealing with, we plot a histogram for each numeric attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MtrZ4dH27XpIkLSL2EJIkSeqoJPsClwIXVdWWJMuAbbN22w4c+DDvXQusBZiY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\noiAEk/eFJGcsHdrbWvuu1tppg3VrvU/vn+SLS35+X2vttNb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AoENaa9cnqTU2ecNWxQIA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\nJElSx/w/At0vd9Q1FWQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26dda41d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indicator.hist(bins=50, figsize=(20, 15))\n",
    "plt.savefig('numeric_attributes.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations: \n",
    "\n",
    "These attributes have very different scales, we will need to apply feature scaling.\n",
    "\n",
    "Many histogram are right skewed. This may make it harder for some machine learning algorithms to detect patterns. We will need to transform them to more normal distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check for correlation between attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i2BVSvMAL2FVWfl9K+SfLND5FuSKHEDSFPMxkSvQ2XAwGOup8bG2PMJUu0R0N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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26dd6547898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"GDP_per_capita\", \"Hours_do_tax\", \"Days_reg_bus\", \"Cost_start_Bus\",\n",
    "              \"Bus_tax_rate\", \"Ease_Bus\"]\n",
    "scatter_matrix(indicator[attributes], figsize=(12, 8))\n",
    "plt.savefig(\"scatter_matrix_plot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems GDP per Capita has a negative correlation with Ease of doing business. The other attributes all have a positive correlation with Ease of doing business. Let's find the most promising attribute to predict the Ease of doing business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "X = indicator.drop(['country', 'Ease_Bus'], axis=1)\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X, indicator.Ease_Bus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated intercept coefficient: 59.9441729113\n"
     ]
    }
   ],
   "source": [
    "print('Estimated intercept coefficient:', regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of coefficients: 5\n"
     ]
    }
   ],
   "source": [
    "print('Number of coefficients:', len(regressor.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>est_coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GDP_per_capita</td>\n",
       "      <td>-0.001263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cost_start_Bus</td>\n",
       "      <td>0.335086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Days_reg_bus</td>\n",
       "      <td>3.920190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bus_tax_rate</td>\n",
       "      <td>0.175034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hours_do_tax</td>\n",
       "      <td>0.033977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         features  est_coef\n",
       "0  GDP_per_capita -0.001263\n",
       "1  Cost_start_Bus  0.335086\n",
       "2    Days_reg_bus  3.920190\n",
       "3    Bus_tax_rate  0.175034\n",
       "4    Hours_do_tax  0.033977"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(X.columns, regressor.coef_)), columns = ['features', 'est_coef'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most promising attribute to predict the \"ease of doing business\" is the \"days spent to register a business\", so let’s zoom in on their correlation scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MuTc84KmOxW8a89zuNtavOZEXzOliYU87L5jTxfo1J7aM8jUMr3hVIG8E/lJV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\nMQzDMOrCFIhhGIZRF/8fb70pQygWM7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26dd65ce8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indicator.plot(kind=\"scatter\", x=\"Days_reg_bus\", y=\"Ease_Bus\",\n",
    "             alpha=0.8)\n",
    "plt.savefig('scatter_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation is indeed very strong; you can clearly see the upward trend and the points are not too dispersed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "y = indicator.Ease_Bus\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a Linear Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60703279859099579"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So in our model, 60.7% of the variability in Y can be explained using X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.58752374172286"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "regressor_mse = mean_squared_error(y_pred, y_test)\n",
    "\n",
    "import math\n",
    "math.sqrt(regressor_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we are an average of 33.59 away from the ground true score on \"ease of doing business\" when making predictions on our test set.\n",
    "\n",
    "The median score of \"Ease of doing business\" is 95, so a typical prediction error of 33.59 is not very satisfying. This is an example of a model underfitting the training data. When this happens it can mean that the features do not provide enough information to make good predictions, or that the model is not powerful enough. The main ways to fix underfitting are to select more features from Wordbank indicators(e.g., \"getting credit\", 'registering property\" and so on). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39.73396857])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.predict([[41096.157300, 5.0, 3, 58.7, 161.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Cost_start_Bus</th>\n",
       "      <th>Days_reg_bus</th>\n",
       "      <th>Bus_tax_rate</th>\n",
       "      <th>Hours_do_tax</th>\n",
       "      <th>Ease_Bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>41096.1573</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>58.7</td>\n",
       "      <td>161.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  GDP_per_capita  Cost_start_Bus  Days_reg_bus  Bus_tax_rate  \\\n",
       "13  Belgium      41096.1573             5.0             3          58.7   \n",
       "\n",
       "    Hours_do_tax  Ease_Bus  \n",
       "13         161.0        42  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator.loc[indicator['country'] == 'Belgium']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 26.10332477])"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.predict([[42157.927990, 0.4, 2, 21.0, 131.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Cost_start_Bus</th>\n",
       "      <th>Days_reg_bus</th>\n",
       "      <th>Bus_tax_rate</th>\n",
       "      <th>Hours_do_tax</th>\n",
       "      <th>Ease_Bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Canada</td>\n",
       "      <td>42157.92799</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  GDP_per_capita  Cost_start_Bus  Days_reg_bus  Bus_tax_rate  \\\n",
       "27  Canada     42157.92799             0.4             2          21.0   \n",
       "\n",
       "    Hours_do_tax  Ease_Bus  \n",
       "27         131.0        22  "
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator.loc[indicator['country'] == 'Canada']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KD5fes2ZNhnAaNn8+0F84BdEIaj1s2Ork1Bd1qdGIiADLgGnAu1W1w+e48cB2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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26dda632048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(regressor.predict(X_train), regressor.predict(X_train)-y_train, c='indianred', s=40)\n",
    "plt.scatter(regressor.predict(X_test), regressor.predict(X_test)-y_test, c='b', s=40)\n",
    "plt.hlines(y=0, xmin=0, xmax=200)\n",
    "plt.title('Residual plot using training(red) and test(blue) data')\n",
    "plt.ylabel('Residual')\n",
    "plt.savefig('residual_plot.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
